[whatmodel.sh](../../README.md) / Starling-LM 7B alpha
# Starling-LM 7B alpha
UC Berkeley (NEST) · released 2023-11 · 7.24B params · apache-2.0
**score 41.1** ±1.1 · #545 of 732 · from 11 benchmark results across 8 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Starling-LM 7B alpha among every model on that task or test, worst to best.
task vs the others rank counts tests
chat & writing ▁▁▁▃●▄▆█▆▇▇▃ #344 of 402 65% 4
chat & writing · #344 of 402 · 65% of its score
Beats [OpenThinker3 7B](openthinker3-7b.md), [Code Llama 70B Instruct](codellama-70b-instruct.md), [Gemma 4 E2B](gemma-4-e2b.md) here, though they rank higher overall. Trails [Granite 4.0 H 350M](granite-4-0-h-350m.md), [LFM2 350M](lfm2-350m.md), [Gemma 3 1B](gemma-3-1b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AlpacaEval 2 length-controlled 14.7% ▁▂▂▃●▆█▆▄▄▁▁ beats 37/122 medium · avg 35% 28% sharp
Arena-Hard (v0.1, GPT-4-1106 judg… 12.8% ▂▁▁●█▅▄▃▃▅▃▁ beats 7/60 medium · avg 47% 15% sharp
LMArena (text) 1167.5 ▁▁▁▂●▃▃▆▆▇█▃ beats 48/385 avg 1260 12% blunt
Polyrating rating (bias-aware re-… 1099.0 ▂▁▂▁▃▇●▆▇▄▂▂ beats 44/95 avg 1187 11% typical
other tests (7)
test result vs the others in its run difficulty counts why
Natural Questions (closed book) 28.2% ▁·▁·▁▂▁▂·▅▂● beats 25/28 medium · avg 48% 12% at its level
CPTUB average (SpeakLeash, 0-5 ju… 52.6% ▁▂▂▂●█▃▆▅▄▂▃ beats 18/95 medium · avg 65% 10% at its level
Polish EQ-Bench v2 (SpeakLeash) 46.3% ▁··▁▁▂▂●▅▆█▆ beats 19/70 medium · avg 62% 7.0% at its level
Open PT-LLM Leaderboard average (… 69.6% ▃▁▁▁▁▁▁▁▂●▅▁ beats 162/276 medium · avg 72% 2.7% at its level · noisy
TriviaQA 66.0% ▁··▁█▃▃▁▄▅▄● beats 26/28 easy · avg 73% 1.3% at its level · noisy
HaluEval QA hallucination detecti… 59.7% █▁▁·▁▁·▁▁●▁▁ beats 24/27 hard · avg 66% 1.1% noisy
TruthfulQA MC2 47.3% ▅▃█▆●▂▅···▁▁ beats 22/31 medium · avg 57% 0.3% blunt · at its level
source results share of the score
AlpacaEval 1.0 and 2.0 leaderboards 1 28% ▇▇▇
Hallucinations Leaderboard results 4 15% ▇▇
Arena-Hard-Auto v0.1 leaderboard 1 15% ▇▇
LMArena leaderboard dataset 1 12% ▇
Polyrating: A Cost-Effective and Bias-A… 1 11% ▇
SpeakLeash CPTUB 1 10% ▇
SpeakLeash Polish EQ-Bench leaderboard 1 7% ▇
Open Portuguese LLM Leaderboard 1 3% ▇
prior: pull toward the middle 0% ▇
weighted average = 41.1
Licences: AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Hallucinations Leaderboard results ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Arena-Hard-Auto v0.1 leaderboard ([Apache-2.0](../../licenses/arena-hard-auto-v0-1-Apache-2.0.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · SpeakLeash CPTUB ([MIT](../../licenses/hf-leaderboard-spaces-MIT.txt)) · SpeakLeash Polish EQ-Bench leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run Starling-LM 7B alpha (Q4_K_M, needs ~4.4 GB, from [huggingface.co/MaziyarPanahi](https://huggingface.co/MaziyarPanahi/Starling-LM-7B-alpha-GGUF/tree/dee7ac52e1114625498ed1a0c3defe06c07ab993))
```sh
llama cli -hf MaziyarPanahi/Starling-LM-7B-alpha-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf MaziyarPanahi/Starling-LM-7B-alpha-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.7 GB) `llama cli -hf MaziyarPanahi/Starling-LM-7B-alpha-GGUF:Q8_0`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run Starling-LM 7B alpha (Q4_K_M, needs ~4.4 GB, from [huggingface.co/MaziyarPanahi](https://huggingface.co/MaziyarPanahi/Starling-LM-7B-alpha-GGUF/tree/dee7ac52e1114625498ed1a0c3defe06c07ab993))
```sh
lms get https://huggingface.co/MaziyarPanahi/Starling-LM-7B-alpha-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~7.7 GB) `lms get https://huggingface.co/MaziyarPanahi/Starling-LM-7B-alpha-GGUF@Q8_0 -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run Starling-LM 7B alpha (Q4_K_M, needs ~4.84 GB, from [ollama.com](https://ollama.com/library/starling-lm/tags))
```sh
ollama run starling-lm:7b-alpha-q4_K_M
```
Other versions: Q8_0 (~9.19 GB) `ollama run starling-lm:7b-alpha-q8_0`
Model repository and documentation: [huggingface.co/berkeley-nest](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha)
[interactive version](https://whatmodel.sh/model/starling-lm-7b-alpha) · [JSON](https://whatmodel.sh/api?format=json&all=1)